Solar Plants

Data Import

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
from folium import plugins
import folium


pd.set_option('display.float_format', lambda x: '%0.4f' % x)

US Power Plants Data

gisfilepath = "/Users/jnapolitano/Projects/gas.jnapolitano.io/source/data/Power_Plants.geojson"

powerplants_df = gpd.read_file(gisfilepath)
#Selecting only Operational Plants
powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()
na = powerplants_df.COAL_USED.min()
powerplants_df.replace(na, 0 , inplace=True)

#powerplants_df = powerplants_df.to_crs(epsg=3857)

powerplants_df.describe()

	OBJECTID	LATITUDE	LONGITUDE	OPER_CAP	SUMMER_CAP	WINTER_CAP	PLAN_CAP	RETIRE_CAP	GEN_UNITS	PLAN_UNIT	RETIR_UNIT	COAL_USED	NGAS_USED	OIL_USED	NET_GEN	CAP_FACTOR	LINES	SOURCE_LAT	SOURC_LONG
count	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000	8736.0000
mean	5709.4960	39.0405	-95.0667	134.5691	123.2273	127.9356	1.5068	12.3725	2.3368	0.0144	0.2098	75285.8993	1351867.2459	3590.2573	466814.5072	0.0371	0.3489	39.0424	-94.1651
std	3315.5893	5.5191	19.0667	367.0281	339.7523	350.9757	36.1032	100.9833	2.8505	0.1797	0.9739	555638.6353	6289781.4216	57026.9476	1813781.4037	12.5042	0.5880	5.5178	21.7038
min	1.0000	18.9737	-171.7124	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	-3465212.0000	-1141.5514	0.0000	18.9742	-171.7124
25%	2666.7500	35.1990	-113.7045	2.7000	2.5000	2.4000	0.0000	0.0000	1.0000	0.0000	0.0000	0.0000	0.0000	0.0000	2141.7500	0.0574	0.0000	35.1986	-113.6161
50%	5832.5000	39.4456	-91.6117	9.6000	8.7500	8.8000	0.0000	0.0000	1.0000	0.0000	0.0000	0.0000	0.0000	0.0000	11727.0000	0.2137	0.0000	39.4492	-91.0609
75%	8566.2500	42.4995	-78.6045	74.8000	70.0000	72.0000	0.0000	0.0000	3.0000	0.0000	0.0000	0.0000	0.0000	0.0000	133402.5000	0.4291	1.0000	42.5003	-78.2997
max	11810.0000	71.2921	-67.4016	6809.0000	7079.0000	7079.0000	2200.0000	3623.0000	73.0000	5.0000	28.0000	10040760.0000	138014506.0000	4130642.0000	32377477.0000	4.6214	11.0000	71.2920	36.2439

Identifying the Solar Plants in the Dataset

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
solar_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'SUN'].copy()

#concatenating coal type dfs into one
solar_plants.describe()

	OBJECTID	LATITUDE	LONGITUDE	OPER_CAP	SUMMER_CAP	WINTER_CAP	PLAN_CAP	RETIRE_CAP	GEN_UNITS	PLAN_UNIT	RETIR_UNIT	COAL_USED	NGAS_USED	OIL_USED	NET_GEN	CAP_FACTOR	LINES	SOURCE_LAT	SOURC_LONG
count	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000	2834.0000
mean	7157.9675	37.7763	-92.9739	10.4763	10.2730	10.0978	0.3627	0.1403	1.1401	0.0056	0.0106	0.0000	1134.2445	0.0000	16101.9108	-0.1025	0.0212	37.7813	-91.8098
std	3684.7414	4.1140	19.5182	30.1760	29.9266	29.7078	11.3019	5.5945	1.0293	0.0919	0.1546	0.0000	18862.3855	0.0000	62440.4517	3.9574	0.1464	4.1213	22.5932
min	116.0000	20.7667	-159.5817	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	-2611.0000	-114.1551	0.0000	20.7667	-159.5817
25%	3190.5000	34.8295	-116.5268	1.5000	1.5000	1.4000	0.0000	0.0000	1.0000	0.0000	0.0000	0.0000	0.0000	0.0000	1004.2500	0.0528	0.0000	34.8240	-116.4832
50%	9028.5000	36.7623	-83.1576	3.0000	3.0000	2.8500	0.0000	0.0000	1.0000	0.0000	0.0000	0.0000	0.0000	0.0000	3275.5000	0.1667	0.0000	36.7784	-82.1681
75%	10131.7500	41.3015	-76.3683	5.0000	5.0000	5.0000	0.0000	0.0000	1.0000	0.0000	0.0000	0.0000	0.0000	0.0000	8676.0000	0.2081	0.0000	41.3208	-76.1351
max	11810.0000	47.5475	-69.6717	585.9000	585.9000	585.9000	400.0000	290.0000	16.0000	3.0000	6.0000	0.0000	462108.0000	0.0000	1265760.0000	4.6214	2.0000	47.5475	33.6561

Solar Plants Interactive Map

{eval-rst}

.. index::
   single: Solar Plants Interactive Map

solar_map = solar_plants.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
solar_map

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Solar Plant Density Interactive Map

{eval-rst}

.. index::
   single: Solar Plant Density Interactive Map

solar_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in solar_plants.geometry ]
#

solar_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

solar_heat_layer = plugins.HeatMap(data = solar_heat_data, show=True)
solar_heat_layer.add_to(solar_density_map)

solar_density_map

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